Optimization offarm technologies and crop resources

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Crop yield and crop production within a certain territory can be seen as an interaction of many factors. However, crops adapted to certain conditions are an important local resource for crop productivity with a significant influence on yield risk. Crops can respond nonlinearly to changes in their growing conditions, exhibit threshold responses and be subject to combinations of stress factors that affect their growth, development and yield, ttus, climate variability and changes in the frequency of extreme events are important for yield and the stability and quality from year to year. Higher temperature and precipitation variability increase the risk of lower yield, as many experimental and simulation studies have shown (Porter and Semenov 2005).

Over the generations farmers have selected the best cultivars for their use, creating locally well adapted crops, some of which are still in use in agricultural systems and are an important genetic resource for modern crop breeding. Farmers can not only change crops and cultivars but also modify crop management, by changing the sowing date according to the expected seasonal weather, for example, tte seasonal precipitation pattern (onset of rain, duration of rainy season, distribution during crop growing period) is one of the most important pieces of information for farmers in semi-arid regions using rain-fed cropping, especially for low-input systems (Stigter et al. 2005; Ingram et al. 2002; Mati 2000) in developing countries, which enables them to adapt their sowing dates and crop selection. Seasonal forecasts, provided they are reliable enough, are already being successfully used in developed countries at the farm level to adapt seasonal crop planning (Meinke and Stone 2005), but there is still a deficit when it comes to making such information useable for farmers in low-input systems (Salinger et al. 2005). However, seasonal forecasts are already being used in developing countries for yield forecasting to support policy decision making (Hansen and Indeje 2004) or the MARS project of the European Union, which has been extended to the African regions (Rojas et al. 2005).

The role ofcrop modeling in farm technologies

Crop and whole farm system modeling can help farmers significantly in decisionmaking for crop management options and related farm technologies, provided it is used properly and infrastructural support of the standard in developed countries is available. An example is presented by Keating et al., (2003) applying the APSIM model for farming system simulation in Australia. Examples have also been presented for tropical regions such as Asia, where related user-friendly software has been developed (Aggarwal et al. 2006a,b).

However, for medium- and low-input systems in developing countries crop or agroecosystem modeling is currently used mainly to guide general decision-making on a higher institutional or farm-advising level. Matthews et al. (1997), for example, reported that for rice production in Asia the modification of sowing dates at high latitudes, where higher temperatures allowed a longer potential crop-growing season, permitted a transition from single cropping to double cropping in some locations, which could had a significant effect on regional production. Two shorter ripening varieties might be a better strategy than a longer maturing variety because the grain formation and ripening periods are pushed to less favorable conditions later in the season. Planting dates could also be adjusted to avoid high temperatures at the time of flowering (spikelet sterility). Spikelet sterility resistance of cultivars to temperature is another option for reducing the yield risk for rice under high or increasing climate variability. Further examples are changes to more heat resistance or earlier ripening cultivars, as it has been shown that heat stress can significantly reduce crop yield (Southworth et al. 2000; Soja et al. 2005). For crops in tropical regions, e.g. soybean in India, a delay in the sowing date has been recommended for similar reasons (Mall et al. 2004).

Climatic variability influences not only the production of individual crops but also the agriculture systems, which are composed of several interdependent segments. For example, with grassland or cereal production considerable variations owing to climatic factors might be of major importance to dairy farmers (or dairy unit of the farm) (Holden and Brereton 2002). tterefore the whole system must allow for the risk of unfavorable weather conditions, ttis requires stocking up necessary reserves and possibly purchasing forage if this cannot be produced locally. Prior knowledge of such a need (e.g. deficit or surplus in crop production) might enable the subject to obtain a better price or the state agency to prepare to intervene. tte case of Austrian grassland production in 2001 and 2003 could serve us as an example, showing how such an "early warning system" can work (Fig. 10.5). As most of the yield variability is caused by climatic factors and their interaction with soil conditions, sward composition and management a relatively simple model of the whole system could be created (Trnka et al., 2006). If such system is then combined with appropriate GIS information it might be updated (and regionalized) in order to identify regions where forage growth has became critically low (Fig. 10.5). When such a model is coupled with a weather generator a probabilistic forecast can be issued early in the season, allowing farmer to better estimate the chance of a good/poor harvest (Fig. 10.5) or prepare to set up irrigation technologies.

remaining days to the harvest

Fig. 10.5 ^e high resolution GIS map of Austrian grassland yields during the unusually dry year 2003 documents extremely low yield areas b) Example of the yield forecast for cut 2 (from 22 May to 26 July 2002) at Gumpenstein experimental station, ^e horizontal line represents the level of observed dry matter yield at the site, ^e orange vertical bars represent yield predictions based on three statistical forecasting methods, ^e green bars represent probabilistic forecasts, each based on the 99 GRAM model runs issued on the given day preceding harvest, ^e x-axis description depicts the number of days to the harvest, ^e lowest and highest parts of each bar represent minimum and maximum predicted yields, ^e white part of each bar indicates mean value ± standard deviation (SD).

remaining days to the harvest

Fig. 10.5 ^e high resolution GIS map of Austrian grassland yields during the unusually dry year 2003 documents extremely low yield areas b) Example of the yield forecast for cut 2 (from 22 May to 26 July 2002) at Gumpenstein experimental station, ^e horizontal line represents the level of observed dry matter yield at the site, ^e orange vertical bars represent yield predictions based on three statistical forecasting methods, ^e green bars represent probabilistic forecasts, each based on the 99 GRAM model runs issued on the given day preceding harvest, ^e x-axis description depicts the number of days to the harvest, ^e lowest and highest parts of each bar represent minimum and maximum predicted yields, ^e white part of each bar indicates mean value ± standard deviation (SD).

In fact this method performs better than the standard "statistical" yield prediction and can be performed with reasonable accuracy relatively early in the season. Forecast precision could be improved by issuing a probabilistic forecast that incorporates a long-term weather forecast for the rest of the season.

Changing agricultural systems and the role offarm technologies

Crop response to environmental conditions is a complex problem. Beside the seasonal weather, crop characteristics and management, crop yield is influenced by soil and terrain properties, fertilization (especially nitrogen, phosphorus and potassium), pests and diseases pressure as well as soil cultivation. All these factors can alter with time and changing production systems and interact with farm technologies. For example, in the emerging farming systems with lower than optimum doses of mineral fertilizers (as in ecological farming systems) yield has been found to be more directly related to crop rotation schemes (for example to the percentage of perennial legumes used in case of cereals). One per cent use of forage crops (mostly legumes) caused growth in grain yield of 23 kg/ha1 (Sroller et al. 2002). Ecological farming systems, for example, use more complex crop rotation, different soil cultivation and crop protection measures and finally different technologies than in conventional farming.

A comparative study of the past decades in Central Europe (including 10 Western and former Soviet countries) presented by Chloupek et al. (2004) claims that the influence of cultivars was relatively low in comparison with the influences of location, year, nitrogen application, use of growth regulators and fungicides (Sip et al. 2000) in relation to yield increase. However, their impact increased between 1962 and 1992 from 25-30 to 50 percent (Bares et al. 1995). In the 1950s, Fischbeck (1999) reported that yield growth for wheat in Germany was due to increased nitrogen fertilization, and later due to chemicals used to shorten straw and to fungicides. Factors influencing crop yields in the neighboring Czech Republic were summarized by Vrkoc (1992) for 1948-1990. He reported that the most important factors were the decreasing influence of inherent soil fertility (40-0%); the decreasing influence of weather (20-0%); the relatively stable influence of cultural practices (10-25%); and the increasing influence of varieties, fertilizers and plant protection practices (during the period: 5-30, 10-25 and 5-20%, respectively), ttis is a view also expressed by Chloupek et al. (2004) who claims increasing yield stability. Even though some additional findings support these claims, e.g. increases in minimum regional yields level or decrease in interregional differences during individual seasons (Fig. 10.6) the claims of decreasing weather influence even in high- and medium-input agriculture depend to a large extent on the responses of crop cultivars to the prevailing climate and weather conditions or their degree of adaptation to these conditions. For example, it has been shown that those crops exhibiting the highest increase in yield were also the most adaptable to inter-annual weather variability, cultivars grown and cultivation techniques used.

In a simulation study we found that a relatively large proportion of yield variability could be explained simply at least in some regions by the monthly drought

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Fig. 10.6. Development of the mean national yield (dot) of two major cereal crops (spring barley and winter wheat) during the period 1961-2000. ^e bars represent the minimum and maximum yields obtained at the regional level (country composed of 78 individual regions). Linear trends are provided separatelyfor 1961-1990 and 1991-2000.

index. It showed that too dry/wet conditions during the growing season significantly reduced yields (Fig. 10.7). In another study Trnka (2006) demonstrated that interseasonal permanent grassland yield variability was mostly a function of varying global radiation, temperature and soil moisture regimes even with relatively intense production under the temperate Central European conditions.

As mentioned above, weather and/or climate and optimisation of farming technologies are not the only drivers influencing agriculture systems, their productivity and even their sustainability. Optimisation strategies in agriculture might, for example, include changes in land use, shift of production areas because of climate shifts (Seguin, 2003), changing the size of farms and fields in combination with technology, soil amelioration and sustained improvements of inputs or methods (e.g. better adapted cultivars, machines, chemicals, fertilisers) additionally triggered by policy and governmental incentive measures. Combining these manyfold impacts might result in different sensitivities of the agriculture systems to weather/ climate factors, with positive and negative effects .

tte effect of these factors over extended periods, for example the past five decades, can be demonstrated by looking at farming systems in the Czech Republic (during the change from the Communist regime to a democratic system), tte large farms and high investment (although in many cases ineffective) together with the

Fig. 10.7. Time series of the relative Palmer Z index averaged over the period April-June of each year and detrended spring barley yield for the south-eastern part of the Czech Republic, ^e blue arrows mark unusually wet seasons and red arrows for seasons with unusually dry growing periods.

intensive use of fertilizers and pesticides triggered by the policy-oriented yield-maximizing strategy produced a sustained increase in yields (as shown in Fig. 10.6 for cereals) in the Czech Republic, which from 1961 to 1990 was comparable with or higher than the mean of the EU-15, despite the comparatively worse climate and soil conditions (Chloupek et al. 2004). Even in the late 1980s when the intensity of the Czech Republic's agricultural production was at its peak, the effect of environmental factors on production remained high (as has also been experienced in other European countries with different agricultural structures), as can be seen from the yield variability at the district level compared with the national mean yield (Fig 10.6a-b). tte change of the political system in 1989-1990 led to the introduction of market economy principles, which forced producers to put the emphasis back on productivity and sustainable production rather than maximizing yield. Some of the least fertile areas were thus turned back to grasslands or forests and the amount of fertilizers and pesticides decreased dramatically, ttis change led to a decrease in national mean yields, especially in the case of spring barley, where in Western countries like Austria a continuously increasing yield trend has been observed, driven mainly by productivity. Only during the past two decades has sustainability and environmental protection been forced either by policy measures or by changes in farmers' strategy in response to market demand, such as in ecological farming systems, tte latter cases have led to a diversification of yield levels, depending on the intensity of production, and productivity (driven by market prices and government support).

As Bares et al. (1995) and Sroller et al. (2002) noted, the influence of new culti-vars has been increasing over past 40 years, especially in high- and medium-input farming systems, tte continuous effort of crop breeders in close cooperation with state authorities in charge of approval of newly bred cultivars can be regarded as one of the most important drivers of increasing productivity in EU and Central European agriculture in the past 40 years. As Chloupek et al. (2004) showed for the

Fig. 10.8. Development of the mean national yield (circles) of winter wheat in the Czech Republic from 1961 to 2000 and the mean attainable yield (based on8113 yield experiments from over 40 State Institute for Agriculture Supervision and Testing sites). Trends lines are provided to document development ofboth yield series over time.

Fig. 10.8. Development of the mean national yield (circles) of winter wheat in the Czech Republic from 1961 to 2000 and the mean attainable yield (based on8113 yield experiments from over 40 State Institute for Agriculture Supervision and Testing sites). Trends lines are provided to document development ofboth yield series over time.

Czech Republic, the yield of all major crops increased steadily during the period 1961-2000 (although there has been a depression during the past decade because of a system change as mentioned above).

ttis is partly explained by the other farming techniques such as appropriate application of nitrogen, pesticides and better technology, but all of these intensification factors can be utilized only when proper cultivars are used. For some crops (e.g. winter wheat) we noted that the positive trend in maximum attainable yields (i.e. level of yields from the mix of the newly introduced hybrids grown in near-optimum soil conditions with high standard of farming practices) was almost the same as the national mean yield trend (Fig. 10.8). Interestingly, from 1971 until the late 1980s there was quite a large increase in the maximum attainable yields whereas this breeding progress was much less pronounced in 1990s, tte present national mean yield is only about 60 per cent of attainable experimental yields (Fig. 10.8), and thus 40 per cent of the cultivar production potential is theoretically not being utilized (in the case of winter wheat), ttis could be a result of the less ideal soil conditions compared with the experimental sites but is probably attributable for the most part to the reduced application of optimum conditions for reaching maximum yields (nutrient and pest/diseases constraints) in order to optimize productivity.

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